Memristive Neuromorphic Interfaces: Integrating Sensory Modalities with Artificial Neural Networks

Abstract

The advent of the Internet of Things (IoT) has led to an exponential growth in data generated from sensors. Consequently, a time- and energy-efficient method for processing complex and unstructured external information is required. Unlike conventional von Neumann sensory systems with separate data collection and processing units, biological sensory systems integrate sensing, memory, and computing to process environmental information in real-time with high efficiency. The memristive neuromorphic sensory systems, which use memristors as basic components, have emerged as promising alternatives to CMOS-based systems. Memristors can closely replicate key characteristics of biological receptors, neurons, and synapses by integrating threshold and adaptation properties in receptors, action potential firing in neurons, and synaptic plasticity in synapses. Furthermore, through the careful engineering of their switching dynamics, the electrical properties of memristors can be tailored to emulate specific functions, all while benefiting from high operational speed, low power consumption, and exceptional scalability. Consequently, their integration with high-performance sensors offers a promising pathway toward realizing fully integrated artificial sensory systems that can efficiently process and respond to diverse environmental stimuli in real time. In this review, we first introduce the fundamental principles of memristive neuromorphic technologies for artificial sensory systems, explaining how each component is structured and what functions they perform. We then discuss how these principles are applied to replicate the four traditional senses, highlighting the underlying mechanisms and recent advances in mimicking biological sensory functions. Lastly, we address the remaining challenges and provide future prospects for the continued development of memristor-based artificial sensory systems.